nf-core/nanoseq

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Introduction

nfcore/nanoseq is a bioinformatics analysis pipeline for Nanopore DNA/RNA sequencing data that can be used to perform basecalling, demultiplexing, QC, mapping and downstream analysis.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

1. Read QC (FastQC)
2. Present QC for raw reads (MultiQC)

On release, automated continuous integration tests run the pipeline on a full-sized dataset obtained from the Singapore Nanopore Expression Consortium on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline Summary

1. Basecalling and/or demultiplexing (Guppy or qcat; optional)
2. Sequencing QC (pycoQC, NanoPlot)
3. Raw read DNA cleaning (NanoLyse; optional)
4. Raw read QC (NanoPlot, FastQC)
5. Alignment (GraphMap2 or minimap2)
   • Both aligners are capable of performing unspliced and spliced alignment. Sensible defaults will be applied automatically based on a combination of the input data and user-specified parameters
   • Each sample can be mapped to its own reference genome if multiplexed in this way
   • Convert SAM to co-ordinate sorted BAM and obtain mapping metrics (SAMtools)
6. Create bigWig (BEDTools, bedGraphToBigWig) and bigBed (BEDTools, bedToBigBed) coverage tracks for visualisation
7. RNA-specific downstream analysis:
   • Transcript reconstruction and quantification (bambu or StringTie2)
     • bambu performs both transcript reconstruction and quantification.
     • When StringTie2 is chosen, each sample can be processed individually and combined. After which, featureCounts will be used for both gene and transcript quantification.
   • Differential expression analysis (DESeq2 or DEXSeq)
8. Present QC for raw read and alignment results (MultiQC)

Quick Start

1. Install Nextflow (>=21.04.0)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/nanoseq -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>

   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to be able to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

Documentation

The nf-core/nanoseq pipeline comes with documentation about the pipeline usage, parameters and output.

nextflow run nf-core/nanoseq \
    --input samplesheet.csv \
    --protocol DNA \
    --input_path ./fast5/ \
    --flowcell FLO-MIN106 \
    --kit SQK-LSK109 \
    --barcode_kit SQK-PBK004 \
    -profile <docker/singularity/podman/institute>

See usage docs for all of the available options when running the pipeline.

An example input samplesheet for performing both basecalling and demultiplexing can be found here.

Credits

nf-core/nanoseq was originally written by Chelsea Sawyer and Harshil Patel from The Bioinformatics & Biostatistics Group for use at The Francis Crick Institute, London. Other primary contributors include Laura Wratten, Ying Chen, Yuk Kei Wan and Jonathan Goeke from the Genome Institute of Singapore, Johannes Alneberg and Franziska Bonath from SciLifeLab, Sweden.

Many thanks to others who have helped out along the way too, including (but not limited to): @crickbabs, @AnnaSyme.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on Slack (you can join with this invite).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.